Demostart (in progress)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 September 2024
- Authors
- Maria Bauza, Jose Enrique Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"We trained our agents with a batch size of 256. Agents were trained until convergence which typically took under 10M learner updates but took 32M learner updates on the Screwdriver in cup task."
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Demostart (in progress) was published by Google DeepMind, in United States of America, in September 2024. industry is the category the publisher falls under.
It works in Robotics, and is recorded as doing robotic manipulation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Demostart (in progress) — common questions
Is Demostart (in progress) open source?
No. Demostart (in progress) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Demostart (in progress) have?
No parameter count has been published for Demostart (in progress), which is why no memory or speed figure appears on this page.
Who created Demostart (in progress)?
Demostart (in progress) was published by Google DeepMind, based in United States of America, categorised as industry.
When was Demostart (in progress) released?
Demostart (in progress) was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Demostart (in progress) used for?
Demostart (in progress) works in Robotics, and is recorded as handling robotic manipulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Demostart (in progress)?
None. Demostart (in progress) is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.